Towards Understanding Autism Heterogeneity: Identifying Clinical Subgroups and Neuroanatomical Deviations

Abstract

Autism Spectrum Disorder ('autism') is a neurodevelopmental condition characterized by substantial behavioural and neuroanatomical heterogeneity. This poses significant challenges to understanding its neurobiological mechanisms and developing effective interventions. Identifying phenotypically more homogeneous subgroups and shifting the focus from average group differences to individuals is a promising approach to addressing heterogeneity. In the present study, we aimed to parse clinical and neuroanatomical heterogeneity in autism by combining clustering of clinical features with normative modeling based on neuroanatomical measures (cortical thickness [CT] and subcortical volume) within the ABIDE datasets (N autism=861, N neurotypical=886, age-range 5-64). First, model-based clustering was applied to autistic symptoms as measured by the Autism Diagnostic Observation Schedule to identify clinical subgroups of autism. Next, we examined whether clinical subgrouping resulted in increased neurobiological homogeneity within the subgroups compared to the entire autism group by comparing their spatial overlap of neuroanatomical deviations. We further investigated whether the identified subgroups improved the accuracy of autism classification based on the neuroanatomical deviations using supervised machine learning with cross-validation. Results yielded two clinical subgroups primarily differing in restrictive and repetitive behaviours (RRB). Both subgroups showed increased homogeneity in localized deviations with the high-RRB subgroup showing increased volume deviations in cerebellum and the low-RRB subgroup showing decreased CT deviations predominantly in the postcentral gyrus and fusiform cortex. Nevertheless, substantial within-group heterogeneity remained highlighted by the failure of the classifier to distinguish between the subgroups. Identifying subgroups of autism has substantial clinical implications opening the potential for more tailored behavioural interventions and improving clinical outcomes.